Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data
1Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Center for Molecular Signaling (PZMS), Saarland University, 66421 Homburg, Germany; selina.hemmer@uks.eu (S.H.); sascha.manier@uks.eu (S.K.M.); lea.wagmann@uks.eu (L.W.)
2State Bureau of Criminal Investigation Schleswig-Holstein, 24116 Kiel, Germany; Svenja.Dr.Fischmann@polizei.landsh.de (S.F.); Folker.Dr.Westphal@polizei.landsh.de (F.W.)
*Correspondence: markus.meyer@uks.euAbstract
The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.
1. Introduction
Metabolomics is defined as the analysis of the whole metabolome of a biological system and therefore, aims to detect as many metabolites as possible in a biological sample [1,2]. However, the metabolic profile is not limited to endogenous metabolites but also metabolites of exogenous sources like drugs, diet, and gut microbiota may be added in. Furthermore, metabolomic studies can be divided into two major strategies, untargeted and targeted approaches. Targeted metabolomics usually aims to detect and quantify specific metabolites of known structures. The untargeted or global approach usually aims to identify as many metabolites as possible without having any previous knowledge about them [1,3].
Due to its high selectivity and sensitivity, liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is currently the most commonly applied analytical technique in metabolomics [4,5,6]. To correctly interpret differences in specific metabolites and to gain a proper biological interpretation, a reliable and suitable entire approach is necessary [4,5]. The part of data processing includes a series of steps such as peak detection, peak alignment, baseline correction, and annotation [7,8,9]. Data processing of LC-HRMS raw data is a key step in untargeted metabolomic studies, which establishes a sound basis to accurately identify significant changes. It involves reducing the complexity of raw data by extracting features, and usually transforming them in order to subsequently perform adequate statistical tests [9,10].
A variety of software solutions are available for untargeted data processing, such as the open source software XCMS, MZmine, OpenMS [11], MetAlign, MetaboAnalyst [12] and the commercial software MarkerView, Compound Discoverer (CD), MetaboScape etc. In the case of open source software, modules are often based on the programming language R [7].
Since the underlying algorithms differ, it is very likely that the outcome of a metabolomic study might vary upon the tools used. Li et al. compared the performance of five software solutions (MS-Dial, MZmine, XCMS, MarkerView, and CD) on a benchmark dataset from standard mixtures. All five software solutions revealed similar performance in detecting true features. Nevertheless, to select true discriminating markers, they recommended the combination of MZmine 2 and XCMS [13]. Fernández-Ochoa et al. determined that Agilent Profinder showed good quality of the graphs and was characterized by its ease of use, whereas the R pipeline seemed to be better suited for studies with a large number of samples [7].
Since further studies are missing and the selection of an appropriate tool is essential for the quality and outcome of the statistical evaluation, the present study aimed to compare three different data processing workflows to investigate LC-HRMS data of an untargeted metabolomics study, namely the commercially available software CD 3.1, the open source online tool XCMS Online in combination with MetaboAnalyst 4.0 (XCMS/MetaboAnalyst), and a manually programmed tool using the language R based on different R packages [14]. XCMS, MetaboAnalyst, and the R script were chosen as they were identified as suitable and were successfully used in previous studies [15,16,17]. Simple but highly standardized datasets for evaluation should be used by incubating pooled human liver microsomes (pHLM) with the synthetic cannabinoid A-CHMINACA (1(-cyclohexylmethyl)-N-tricyclo[3.3.1.13,7]dec-1-yl-1H-indazole-3-carboxamide). The outcome of each workflow should be evaluated by its number of significant features, the quality of the peaks, and the results of multivariate statistics. Additionally, the metabolite profile of A-CHMINACA in pHLM should be elucidated.
2. Results and Discussion
2.1. Study Design
Due to the ease of use and low variability of individual pHLM incubations and the fact that it is a very well characterized in vitro model for drug metabolism studies, incubations of pHLM with the synthetic cannabinoid A-CHMINACA were prepared to generate simple datasets [18]. The incubation mixtures were then analyzed using LC-HRMS/MS and finally, three different software tools for untargeted data processing were applied to identify significant features. Software evaluation in this study included the commercial software CD 3.1, which was developed for the used type of MS instrument; open source software workflows including a combination of XCMS Online and MetaboAnalyst 4.0; and a manually programmed tool using R. While XCMS-based software tools might be one of the best solutions for LC-HRMS/MS untargeted metabolomics, XCMS was used as a preprocessing tool in the case of the two open source workflows [15,19,20,21,22]. After data processing, significant features were identified and the metabolic fate of A-CHMINACA in pHLM was elucidated. The three untargeted data processing workflows were extensively evaluated with regards to their number of significant features, the peak quality of the significant feature, their false positive rate, and the results of the multivariate statistics.
2.4. Comparison of the Three Software Workflows
Based on the usage of the three software workflows during this study and the results in the previous sections, an overview of the pros and cons concerning important criteria is given in Table 3.
In comparison to the two open source workflows, the commercial software CD is characterized by its ease of use as a user-friendly black box. Thermo Fisher LC-HRMS/MS RAW files can be uploaded directly, and the desired workflow can be selected. The first results are available after a few mouse clicks. For statistical evaluation, only p-value and fold change have to be specified. Limitations for this kind of workflow are given by the preprocessing parameters, the normalization techniques, and the statistical analysis. Looking at the results in this study, this commercial software showed a low false positive rate for significant features, but neither isotopes nor adducts were detected that usually help in identifying significant features. Since CD is limited to its statistical test of Welch’s t-test, it does not allow one to do statistical evaluation of more than two groups and therefore, it is not a suitable workflow for complex datasets. The open source combination of XCMS Online and MetaboAnalyst 4.0 allowed for more intervention in the processing steps than CD. In XCMS Online, almost all parameters could be taken over with a few exceptions and MetaboAnalyst allowed a wide range of statistical tests. The report of MetaboAnalyst allowed an interpretation of the results. Both online tools were also based on the programming language R, but in contrast to the manually programmed R tool, less programming skills were required. The disadvantage is that due to the limited statistical test equipment of XCMS Online, a combination of these two online platforms was necessary. When comparing the results of the two open source workflows, both showed an almost identical false positive rate. The difference between the two workflows can only be seen in the user handling and the minimal difference in the number of significant features. The latter can be attributed to the not quite perfectly adjusted peak picking parameters using XCMS Online.
In the case of complex datasets, the manually programmed R tool should be the best option. Due to its high number of packages, functions, and methods, it offers a great adaptability also with regard to statistical analysis. However, this open source workflow requires advanced programming skills.
Regarding the results, the most relevant difference between the two open source workflows and the commercial software might be the optimization of the peak picking parameters. In contrast to the two open source workflows, the vendor-based software CD was used without changing any parameter (used as a black box as intended by the manufacturer). On the other hand, the two XCMS-based workflows are neutral for a broad range of data and therefore, they need parameter optimization because they are not usable under standard settings [15,19,20,21].
In summary, all three workflows have found the most important metabolites, but (toxico-)metabolomics includes not only exogenous metabolites but also endogenous ones. However, it must also be said that the investigated set was rather simple and less complex. Due to the minimal fluctuations, it was not necessary to normalize the dataset, for example, using an internal standard. This might be necessary when analyzing plasma or urine samples. The choice of the appropriate method should therefore depend on the complexity of the dataset and on previous knowledge. Complex datasets in this context mean that there are more than two groups in one study and that due to the complexity of the matrix, normalization to an endogenous biomarker is necessary. Previous knowledge basically means that the user has previously programmed with R or other programming languages. The manually programmed R tool required far more programming skills than the open source combination of XCMS Online and MetaboAnalyst 4.0. The commercial software CD on the other hand required almost no prior knowledge of metabolomics data processing.
3. Materials and Methods
3.1. Chemicals and Reagents
A-CHMINACA was provided by the EU project ADEBAR/State Bureau of Criminal Investigation Schleswig-Holstein (Kiel, Germany) for research purpose. The chemical purity and identity of the compound were verified by MS and nuclear magnetic resonance analysis. Ammonium formate, ammonium acetate, formic acid, isocitrate dehydrogenase, isocitrate, dipotassium phosphate, tripotassium phosphate, magnesium chloride, and superoxide dismutase were obtained from Sigma (Taufkirchen, Germany). Acetonitrile (LC-MS grade), methanol (LC-MS grade), and NADP-Na2 were from VWR (Darmstadt, Germany). pHLM (20 mg microsomal protein mL−1) was obtained from Corning (Amsterdam, The Netherlands). After delivery, pHLM were thawed at 37 °C, aliquoted, snap-frozen in liquid nitrogen, and stored at −80 °C until use.
3.2. pHLM Incubation
According to published procedures [16,30,31], incubations using pHLM were prepared as follows. A-CHMINACA was freshly dissolved in methanol and subsequently diluted with 100 mM phosphate buffer to obtain the required concentrations. Incubations were performed at 37 °C using final A-CHMINACA concentrations of 0 (Blank group), 5 (Low group), or 50 µM (High group) and 1 mg protein mL−1 pHLM. Final incubation mixtures also contained 90 mM phosphate buffer, 5 mM isocitrate, 5 mM Mg2+, 1.2 mM NADP+, 200 U mL−1 superoxide dismutase, and 0.5 U mL−1 isocitrate dehydrogenase. The final incubation volume was 50 µL. The reaction was stopped after 60 min by adding 50 µL of ice-cold acetonitrile and then, centrifugated for 2 min at 18,407× g. Every group consisted of five replicates. Pooled quality samples (QC group) were prepared by transferring 10 µL of each incubation into one MS vial. These were also used for optimization of the peak picking parameters, batch correction, and identification of significant features, as described below. An aliquot of 70 µL of the remaining supernatant was transferred into separate MS vials and used for metabolomics analysis, as described below.
3.3. LC-HRMS/MS Apparatus
In accordance with Manier et al. [16], analyses were performed by using a Thermo Fisher Scientific (TF, Dreieich, Germany) Dionex UltiMate 3000 RS pump consisting of a degasser, a quaternary pump, and an UltiMate Autosampler, coupled to a TF Q-Exactive Plus system including a heated electrospray ionization (HESI)-II source. Prior to every experiment, the performance of the columns and mass spectrometer was tested using a test mixture as described by Maurer et al. [32,33]. Gradient normal phase elution was performed on a Macherey-Nagel (Düren, Germany) HILIC Nucleodur column (125 mm × 3 mm, 3 µm) and reversed phase elution using a TF Accucore PhenylHexyl column (100 mm × 2.1 mm, 2.6 µm). The mobile phase and gradient for the PhenylHexyl column consisted of 2 mM aqueous ammonium formate containing acetonitrile (1%, v/v) and formic acid (0.1%, v/v, pH 3, eluent A), as well as 2 mM ammonium formate solution with acetonitrile:methanol (1:1, v/v) containing water (1%, v/v) and formic acid (0.1%, v/v, eluent B). The flow rate was set from 1–10 min to 500 µL min−1 and from 10–13.5 min to 800 µL min−1 using the following gradient: 0–1.0 min hold 99% A, 1–10 min to 1% A, 10–11.5 min hold 1% A, 11.5–13.5 min hold 99% A. The gradient elution for normal phase chromatography was performed using aqueous ammonium acetate (200 mM, eluent C) and acetonitrile containing formic acid (0.1%, v/v, eluent D). The flow rate was set to 500 µL × min−1 using the following gradient: 0–1 min hold 2% C, 1–5 min to 20% C, 5–8.5 min to 60% C, 8.5–10 min hold 60% C, 10–12 min hold 2% C. For preparation and cleaning of the injection system, isopropanol:water (90:10, v/v) was used. Due to the lipophilic properties of A-CHMINACA, eluent D was used for the flushing of both columns. The following settings were used: wash volume, 100 µL; wash speed, 4000 nL s−1; loop wash factor, 2. Column temperature for every analysis was set to 40 °C, maintained by a Dionex UltiMate 3000 RS analytical column heater. Injection volume was set to 1 µL. HESI-II source conditions were as follows: ionization mode, positive or negative; sheath gas, 60 AU; auxiliary gas, 10 AU; sweep gas, 3 AU; spray voltage, 3.5 kV in positive and −4.0 kV in negative mode; heater temperature, 320 °C; ion transfer capillary temperature, 320 °C; and S-lens RF level, 50.0. Mass spectrometry for untargeted metabolomics was performed according to a previously optimized workflow [15,16]. The settings for full scan (FS) data acquisition were as follows: resolution, 140,000 fwhm; microscan, 1; automatic gain control (AGC) target, 5 × 105; maximum injection time, 200 ms; scan range, m/z 50–750; spectrum data type; centroid. Significant features were subsequently identified using PRM. Settings for PRM data acquisition were as follows: resolution, 70,000 fwhm; microscans, 1; AGC target, 5 × 105; maximum injection time, 200 ms; isolation window, 0.4 m/z; collisions energy (CE), 10, 20, 30, or 40 eV; spectrum data type, centroid. The inclusion list contained the monoisotopic masses of all significant features and a time window of their retention time ±60 s. TF Xcalibur software version 3.0.63 was used for data handling. Due to the carry-over effect of A-CHMINACA, the analysis was performed using the following sequence order: five injections of eluent D samples at the beginning of the sequence for apparatus equilibration, followed by five injections of pooled QC samples, five blank groups, five low groups, and five high groups. Additionally, one QC injection was performed every five samples to monitor batch effects, as described by Wehrens et al. [34].
3.4. Dataset Processing with Different Software
For the two open source software workflows, Proteo Wizard was used to convert Thermo Fisher LC-HRMS/MS RAW files into mzXML files [35]. Optimization of the XCMS parameters was done by using a comprehensive parameter sweeping approach [15]. Table S1 summarizes the peak picking and alignment parameters used for the two open source workflows.
In the case of using R, peak picking was performed using XCMS in an R environment [14,36] and the R package CAMERA [37] was used for the annotation of isotopes, adducts, and artifacts. The dataset was filtered keeping merely those features with a p-value using Bonferroni correction [23]. Feature abundances with a value of zero were replaced by the lowest measured abundance as a surrogate limit of detection and the whole dataset was subsequently log10 transformed [34]. Batch correction was performed for those features that were detected in every QC sample. Corresponding feature abundances were corrected using a linear model to extrapolate abundance drift between QC samples [34]. Principal component analysis (PCA) and hierarchical clustering were used to investigate patterns in the dataset. Names for the features were adopted from XCMS using “M” followed by rounded mass and “T” followed by the retention time in seconds. The R script and the mzXML files can be found at https://github.com/sehem/HLM_Metabolomics.git.
For the combination of XCMS Online and MetaboAnalyst 4.0, first, XCMS Online was used for peak picking and alignment using the optimized parameters listed in Table S1. The resulted table of XCMS Online was then processed by removing all features under a retention time of 1 min and above 10 min and all columns were removed except the peak areas of each feature in each sample. The modified table was then uploaded to MetaboAnalyst 4.0 for statistical analysis. For normalization of the dataset, the following settings were used: sample normalization, none; data scaling, none; and data transformation, log transformation. Subsequently, one-way ANOVA was selected using Bonferroni correction for p-value. To investigate patterns in the dataset, PCA and hierarchical clustering using heat maps and dendrograms were selected. For hierarchical clustering, distance measures using Euclidean distances and clustering algorithms using complete were chosen.
In the case of CD, Thermo Fisher LC-HRMS/MS RAW files were uploaded and definitions of study factors in the form of categorical factors were entered. Subsequently, the ratios blank/low, blank/high, and low/high were defined. Afterwards, a predefined untargeted workflow named “Untargeted Metabolomics with statistics detect unknowns with ID using Online Database and mzLogic” was used. This workflow included findings and identified the differences between samples, performed retention time alignment, identified compounds using mzCloud, ChemSpider, and calculated differential analysis such as ANOVA, determined p-values, and fold changes. Bonferroni correction for p-value and fold-change of 1 were used for ANOVA.
3.5. Identification of Significant Features
Identification of significant features was done by recording MS/MS spectra using the PRM method mentioned above. Spectra were imported to NIST MSSEARCH version 2.3, after conversion to mzXML format using ProteoWizard [35]. According to Manier et al. [17], a library search for identification was conducted using the following settings: spectrum search type, identity (MS/MS); precursor ion m/z, in spectrum; spectrum search options, none; presearch, off; other options, none. MS/MS search was conducted using the following settings: precursor tolerance, ±5 ppm; product ion tolerance, ±10 ppm; ignoring peaks around precursor, ±m/z 1. The search was conducted by using the following libraries: NIST 14 (nist_msms and nist_msms2 sublibraries) and Wiley METLIN Mass Spectral Database. Metabolites of the investigated synthetic cannabinoid A-CHMINACA were tentatively identified by interpreting their spectra in comparison to that of the parent compound.
4. Conclusions
In this study, a dataset of pHLM incubations of the synthetic cannabinoid A-CHMINACA was used to evaluate data processing of three different software workflows under their respective optimal parameter settings. The commercial software CD is a vendor-based software, which was specifically developed for the type of MS instrument used in this study. The two open source workflows, XCMS Online/MetaboAnalyst and R, both use the “gold standard” XCMS for peak picking and alignment for untargeted metabolomics data evaluation after LC-HRMS/MS analysis.
While the two open source workflows were highly adaptable methods regarding the optimization of parameters, CD is a user-friendly black box with limited possibilities for optimization. Additionally, the metabolic profile of A-CHMINACA in pHLM was determined to compare the three software solutions. The main metabolic reactions were the hydroxylation of the adamantyl-ring and N-dealkylation of the indazole-3-carbaldeyde moiety.
In relation to the results of this study, CD as an all-in-one solution is characterized by its ease of use and therefore, seems suitable for simple and small metabolomic studies, as the dataset used in this study. However, it is not possible to use the right statistical test, since the dataset exists of three groups. Taking this into account, the statistical results of the used dataset can be better represented with the two open source workflows. Both open source workflows allowed extensive customization but particularly in the case of R, advanced programming skills are required, while XCMS Online/MetaboAnalyst is an almost entirely point-and-click experience. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions. The metabolic fate of A-CHMINACA in pHLM was identified best by the two open source workflows.
Acknowledgments
The authors would like to thank the EU funded project ADEBAR (IZ25-5793-2016-27) for the supply of the chemical standard, as well as Cathy M. Jacobs, Tanja M. Gampfer, Thomas P. Bambauer, Matthias J. Richter, Carsten Schröder, Gabriele Ulrich, and Armin A. Weber for their support.
Appendix Group
Supplementary Materials
The supplementary materials are available online at https://www.mdpi.com/2218-1989/10/9/378/s1.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
| Feature | Measured Mass, m/z | Retention Time, s | Found with | Identity |
|---|---|---|---|---|
| M296T431 | 296.1768 | 431 | XM, CD | A-CHMINACA-M (N-dealkyl-) |
| M424T443 | 424.2610 | 443 | R, XM | A-CHMINACA-M (di-HO-) |
| M408T474 | 408.2661 | 474 | R, XM, CD | A-CHMINACA-M (HO-) |
| M409T474 | 409.2693 | 474 | R, XM | A-CHMINACA-M (HO-) 13C isotope |
| M430T474 | 430.2481 | 474 | R, XM | A-CHMINACA-M (HO-) adduct [M + Na]+ |
| M392T547 | 392.2710 | 547 | R, XM, CD | A-CHMINACA |
| M393T547 | 393.2743 | 547 | R, XM | A-CHMINACA 13C isotope |
| M394T547 | 394.2775 | 547 | R, XM | A-CHMINACA 13C2 isotope |
| M414T547 | 414.2530 | 547 | R, XM, CD | A-CHMINACA adduct [M + Na]+ |
| M415T547 | 415.2562 | 547 | R, XM | A-CHMINACA adduct [M + Na]+ 13C isotope |
| M430T547 | 430.2270 | 547 | R, XM, CD | A-CHMINACA adduct [M + K]+ |
| M437T547 | 437.3290 | 547 | R, XM | A-CHMINACA adduct |
| M438T547 | 438.3320 | 547 | XM | A-CHMINACA adduct 13C isotope |
| Feature | Measured Mass, m/z | Retention Time, s | Found with | Identity |
|---|---|---|---|---|
| M355T70 | 355.2392 | 70 | R, XM | Unknown |
| M430T71 | 430.2270 | 71 | XM | A-CHMINACA adduct [M + K]+ |
| M392T72 | 392.2710 | 72 | R, XM, CD | A-CHMINACA |
| M393T72 | 393.2743 | 72 | R, XM | A-CHMINACA 13C isotope |
| M394T72 | 394.2775 | 72 | R, XM | A-CHMINACA 13C2 isotope |
| M395T72 | 395.2809 | 72 | R, XM | A-CHMINACA 13C3 isotope |
| M356T74 | 356.1802 | 74 | XM | Unknown |
| M135T76 | 135.1174 | 76 | CD | A-CHMINACA artifact (adamantyl-ring) |
| M408T83 | 408.2661 | 83 | R, XM, CD | A-CHMINACA-M (HO-) |
| M409T83 | 409.2693 | 83 | R, XM | A-CHMINACA-M (HO-) 13C isotope |
| M296T86 | 296.1768 | 86 | R, XM, CD | A-CHMINACA-M ( N -dealkyl-) |
| M297T86 | 297.1800 | 86 | XM | A-CHMINACA-M (N-dealkyl-) 13C isotope |
| M408T88 | 408.2661 | 88 | CD | A-CHIMINACA-M (HO-) |
| M422T92 | 422.2453 | 92 | R, XM | A-CHIMINACA-M (HO, Oxo) |
| M424T93 | 424.2610 | 93 | CD | A-CHMINACA-M (di-HO-) |
| M424T96 | 424.2610 | 96 | R, XM, CD | A-CHMINACA-M (di-HO-) |
| M425T96 | 425.2644 | 96 | R, XM | A-CHMINACA-M (di-HO-) 13C isotope |
| M274T113 | 274.1559 | 113 | R, XM | A-CHMINACA-M (HO-) (N-dealkyl-) |
| M312T115 | 312.1715 | 115 | R, XM, CD | A-CHMINACA-M (HO-) ( N -dealkyl-) |
| M146T116 | 146.0819 | 116 | CD | A-CHMINACA artifact (indazole-core) |
| M440T117 | 440.2561 | 117 | CD | A-CHMINACA-M (tri-HO-) |
| M440T122 | 440.2565 | 122 | CD | A-CHMINACA-M (tri-HO-) |
| M176T135 | 176.0924 | 135 | R, XM, CD | Unknown |
| M158T135 | 158.0818 | 135 | R, XM, CD | [M + H − H2O]+175.086 |
| M188T170 | 188.1288 | 170 | R, XM, CD | Unknown |
| M158T174 | 158.0818 | 174 | R, XM | [M + H − H2O]+175.086 |
| M176T174 | 176.0924 | 174 | R, XM, CD | Unknown |
| M341T219 | 341.2447 | 219 | R, XM | Unknown |
| M313T253 | 313.2649 | 253 | R, XM | Unknown |
| M248T270 | 248.2382 | 270 | XM | Unknown |
| Criteria | Compound Discoverer | XCMS Online/MetaboAnalyst 4.0 | Manually Programmed R Tool |
|---|---|---|---|
| Open source | - | + | + |
| Low false-positive rate | + | - | + |
| Flexibility | - | -/+ | + |
| Complex datasets | - | + | + |
| Using raw data | + | - | - |
| Required prior knowledge | - | - | + |
| Annotation of isotopes and adducts | - | + | + |